User define function missbehave...

Hi... I m working with BPNN, And as a reference code i m using this... " ANOOP ACADEMIA – BLOG's BPNN Code "

My problem is, in the for loop after function execute... value's are not updated and result are same... Where is the problem with it... Everything looks fine... I m confuse...where is my mistake... Code is...below

% Assigning the number of hidden neurons in hidden layer
m = 2;

errorValue_theshold = 100;
errorValue = errorValue_theshold + 1; % Only for initial...
delta_V = 0;
delta_W = 0;

[l,b] = size(data);
[n,a] = size(target);

V = rand(l,m); % Weight matrix from Input to Hidden
W = rand(m,n); % Weight matrix from Hidden to Output

count = 0;
itration = 100;

for count = 1: itration

      [errorValue delta_V delta_W] = trainNeuralNet(data,target,V,W,delta_V,delta_W);

      count = count + 1;
      fprintf('Error : %f\n', errorValue);
      fprintf('#itration : %d \t', count);

      Error_Mat(count)=errorValue;

      W = W + delta_W; 
      V = V + delta_V;    

      if errorValue < errorValue_theshold

          fprintf('\n\nFinal Error : %f\n', errorValue);
          fprintf('#totalItration : %d\n', count);

          Error_Mat(count)=errorValue; 

      end
  end

Function Code is : ----------------

          *function [errorValue delta_V delta_W] = trainNeuralNet(Input, Output, V, W, delta_V, delta_W)*             

            Output_of_InputLayer = Input;            

            Input_of_HiddenLayer = V' * Output_of_InputLayer;  % % netj             

            [m n] = size(Input_of_HiddenLayer);

            Output_of_HiddenLayer = 1./(1+exp(-Input_of_HiddenLayer));

            Input_of_OutputLayer = W'*Output_of_HiddenLayer;

            clear m n;             

            [m n] = size(Input_of_OutputLayer);

            Output_of_OutputLayer = 1./(1+exp(-Input_of_OutputLayer));

            difference = Output - Output_of_OutputLayer; 
            square = difference.*difference; 
            errorValue = sqrt(sum(square(:)));             

            clear m n 
            [n a] = size(Output);

            for i = 1 : n 
                for j = 1 : a

                    d(i,j) =(Output(i,j)-Output_of_OutputLayer(i,j))*Output_of_OutputLayer(i,j)*(1-Output_of_OutputLayer(i,j));

                end  
            end

            Y = Output_of_HiddenLayer * d'; 

            if nargin == 4

                delta_W=zeros(size(W)); 
                delta_V=zeros(size(V));

            end

            etta=0.6; alpha=1;

            delta_W= alpha.*delta_W + etta.*Y;             

            error = W*d;

            clear m n 
            [m n] = size(error);

            for i = 1 : m

                for j = 1 :n

                    d_star(i,j)= error(i,j)*Output_of_HiddenLayer(i,j)*(1-Output_of_HiddenLayer(i,j));

                end

            end

            X = Input * d_star';

            delta_V = alpha * delta_V + etta * X;

            end

In Advance... Thanks :)